Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks

Abstract

In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembézar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies.